scholarly journals Structuring electronic dental records through deep learning for a clinical decision support system

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822098003
Author(s):  
Qingxiao Chen ◽  
Xuesi Zhou ◽  
Ji Wu ◽  
Yongsheng Zhou
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Hybrid Method ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Selection Strategy ◽  
Dental Records ◽  
Electronic Dental Records

Extracting information from unstructured clinical text is a fundamental and challenging task in medical informatics. Our study aims to construct a natural language processing (NLP) workflow to extract information from Chinese electronic dental records (EDRs) for clinical decision support systems (CDSSs). We extracted attributes, attribute values, and tooth positions based on an existing ontology from EDRs. A workflow integrating deep learning with keywords was constructed, in which vectors representing texts were unsupervised learned. Specifically, we implemented Sentence2vec to learn sentence vectors and Word2vec to learn word vectors. For attribute recognition, we calculated similarity values among sentence vectors and extracted attributes based on our selection strategy. For attribute value recognition, we expanded the keyword database by calculating similarity values among word vectors to select keywords. Performance of our workflow with the hybrid method was evaluated and compared with keyword-based method and deep learning method. In both attribute and value recognition, the hybrid method outperforms the other two methods in achieving high precision (0.94, 0.94), recall (0.74, 0.82), and F score (0.83, 0.88). Our NLP workflow can efficiently structure narrative text from EDRs, providing accurate input information and a solid foundation for further data-based CDSSs.

scholarly journals AI in Healthcare practice: opportunities and challenges for clinical decision support systems

2019 ◽  
Author(s):  
David R. Millen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Support Systems ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Healthcare Applications

In the past few years there has been great optimism about the potential benefits of incorporating AI (cognitive) capabilities into healthcare products and services. Indeed, progress in Natural Language Processing (NLP) has made electronic health records both more accessible and comprehensible, advances in image processing algorithms has helped to early identify tumors, and large datasets with new discovery services can help with breakthrough insights in life sciences and drug discovery. Importantly, new AI-based solutions are embedded in the sociotechnical systems of clinical care and within complex regulatory environments and globally diverse cultural frameworks. In this talk, I will present several case studies of novel AI – based healthcare applications that have been introduced in recent years and share lessons learned along the way. Particular focus will be on design research challenges for healthcare products, including understanding complex workflows within clinical settings and highly specialized and diverse mental modals, and understanding multiple stakeholders and interdependent participants. Design considerations and emerging opportunities for AI-based clinical decision support systems will also be shared.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

scholarly journals The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S M Jansen-Kosterink ◽  
M Cabrita ◽  
I Flierman
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Focus Groups ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

scholarly journals Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

2021 ◽  
Vol 11 (6) ◽  
pp. 2880
Author(s):  
Miguel Pereira ◽  
Patricia Concheiro-Moscoso ◽  
Alexo López-Álvarez ◽  
Gerardo Baños ◽  
Alejandro Pazos ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Intensive Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Web Of Science ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Real Time Information ◽  
Time Information ◽  
Improve Patient

The advances achieved in recent decades regarding cardiac surgery have led to a new risk that goes beyond surgeons' dexterity; postoperative hours are crucial for cardiac surgery patients and are usually spent in intensive care units (ICUs), where the patients need to be continuously monitored to adjust their treatment. Clinical decision support systems (CDSSs) have been developed to take this real-time information and provide clinical suggestions to physicians in order to reduce medical errors and to improve patient recovery. In this review, an initial total of 499 papers were considered after identification using PubMed, Web of Science, and CINAHL. Twenty-two studies were included after filtering, which included the deletion of duplications and the exclusion of titles or abstracts that were not of real interest. A review of these papers concluded the applicability and advances that CDSSs offer for both doctors and patients. Better prognosis and recovery rates are achieved by using this technology, which has also received high acceptance among most physicians. However, despite the evidence that well-designed CDSSs are effective, they still need to be refined to offer the best assistance possible, which may still take time, despite the promising models that have already been applied in real ICUs.

scholarly journals Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Elizabeth Ford ◽  
Natalie Edelman ◽  
Laura Somers ◽  
Duncan Shrewsbury ◽  
Marcela Lopez Levy ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
General Practitioners ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Structured Interview ◽  
Qualitative Interview Study ◽  
High Level

Abstract Background Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. Methods We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. Results We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. Conclusions Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.

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